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Related Experiment Video

Updated: Jul 6, 2025

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

Automating methods for estimating metabolite volatility.

Laura K Meredith^1,2, S Marshall Ledford³, Kristina Riemer⁴

¹School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States.

Frontiers in Microbiology

|January 1, 2024

Summary

This summary is machine-generated.

A new pipeline, volcalc, estimates metabolite volatility, revealing insights into biological roles and the volatilome. This tool aids in understanding volatile compounds across life

Keywords:

VOCs bioinformatics chemoinformatics metabolic database volatile metabolite volatility

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Published on: July 11, 2014

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Area of Science:

Metabolomics
Cheminformatics
Systems Biology

Background:

Metabolite volatility influences biological functions and detection methods.
Volatility data is scarce for many metabolites, hindering research.
Understanding volatility is key to exploring metabolites as fundamental biological traits.

Purpose of the Study:

To develop an automated method for estimating metabolite volatility.
To create an open pipeline, volcalc, for predicting gas-phase partitioning.
To analyze the distribution and significance of volatile metabolites in biological systems.

Main Methods:

Adapted SIMPOL.1 methods to estimate vapor pressure from molecular functional groups.
Implemented volcalc pipeline using chemoinformatic tools for automated estimates.
Utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for metabolite data.

Main Results:

Achieved 93% accuracy in predicting volatility categories compared to curated data.
Estimated 3.4% (soil) to 26.6% (clean atmosphere) of KEGG compounds as highly volatile.
Identified a core set of 30% shared volatiles across all life domains, with bacteria having the most kingdom-specific volatiles.

Conclusions:

Volcalc provides a foundation for studying the volatilome and the role of volatile metabolites.
The pipeline facilitates hypothesis generation and testing in biological systems.
Volcalc is an accessible tool for integrating volatility analysis with other bioinformatics pipelines.